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Making Space for the Economy: Live Performances, Dead Objects, and Economic Geography

2008· article· en· W1967846188 on OpenAlexaff
Trevor J. Barnes

Bibliographic record

VenueGeography Compass · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsUniversity of British Columbia
FundersRoyal Swedish Academy of Sciences
KeywordsPerformativitySpace (punctuation)Strategic geographyPerspective (graphical)ConstitutionEconomic geographyPoliticsHistorical geographyWork (physics)Critical geographyHuman geographySociologyLocation theoryGeographyEconomyEconomicsPolitical scienceLawGender studiesLinguisticsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract The article explores the usefulness of the recent literature on markets and performativity for economic geography. The article is divided into two main sections. The first reviews work on performativity, the idea that our statements and representations actively produce reality rather than being mere faithful copies of it. Writers in science studies, in particular, have taken up this notion and used it to understand the making of economic markets. The second argues that economic geography usefully amends the work on performance and economic markets by adding a geographical perspective that plays out in at least four registers: the performance of spatial theory; the geographical performance of economic theory; the spatial performance of market constitution; and the political performance of spatial markets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.053
Scholarly communication0.0120.009
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.217
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations68
Published2008
Admission routes1
Has abstractyes

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